Order Flow and Exchange Rate Dynamics in
Electronic Brokerage System Data

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Abstract:

We analyze the association
between order flow and exchange rates using a new dataset
representing a majority of global interdealer transactions in the
two most-traded currency pairs. The data consist of six years
(1999-2004) of order flow and exchange rate data for the
euro-dollar and dollar-yen currency pairs at the one-minute
frequency from EBS, the electronic broking system that now
dominates interdealer spot trading in these currency pairs. This
long span of high-frequency data allows us to gain new insights
about the joint behavior of these series. We first confirm the
presence of a substantial association between interdealer order
flow and exchange rate returns at frequencies ranging from one
minute to one week, but, using our long span of data, we find that
the association is weaker at lower frequencies, with far less
long-term association between cumulative order flow and long-term
exchange rate movements. We study the linearity and time-variation
of the association between high-frequency exchange rate returns and
order flow, and document an intradaily pattern to the relationship:
it is weakest at times when markets are most active. Overall, our
study tends to support the view that, while order flow plays a
crucial role in high-frequency exchange rate movements, its role in
driving long-term fluctuations is much more limited.

1 Introduction

A strong positive contemporaneous association between exchange
rate returns and order flow has been documented in a number of
recent studies. Evans and Lyons (2002), for instance, in a seminal
paper, reported that a regression of Deutsche mark/dollar daily
returns on daily order flow yielded an R in excess of 60%, an amazingly
strong result in the study of price discovery in foreign exchange
markets. Other authors have since confirmed the association between
order flow and returns at daily or intradaily frequencies using
several other foreign exchange datasets. However the various papers
have come to very different conclusions as to the role of order
flow in price discovery in foreign exchange markets and the
permanence of its impact.

On the one hand, Evans and Lyons (2002, 2005, 2006) argue that
much of the impact of order flow in their data is permanent and
that fundamental macroeconomic information is revealed to the
market and becomes embedded in prices via order flow. Love and
Payne (2004) and Marsh and O'Rourke (2005), among others, find that
their data lead them to the same general conclusion. On the other
hand, Froot and Ramadorai (2005), for example, conclude that, in
their data, order flow is associated only with transitory exchange
rate movements and does not convey information to market
participants about macroeconomic fundamentals. They label their
conclusion the ``weak flow-centric'' view, in contrast to Evans and
Lyons' ``strong flow-centric'' view. Among other papers falling
more on the side of Froot and Ramadorai, Breedon and Vitale (2004)
conclude that ``the strong contemporaneous correlation between
order flow and exchange rates is mostly due to liquidity
effects.''

It is difficult to reconcile these sharply-contrasting
conclusions, as the various papers have used datasets with very
different characteristics in their empirical work. The datasets
vary in frequency, span, period of coverage, and segment of the
market in which the order flow is recorded. The work of Evans and
Lyons (2002), for instance, was based on their analysis of 4 months
of high-frequency data from the Reuters direct-dealing interdealer
electronic platform in 1996, and the same data were used in Evans
and Lyons (2006) to study the role of order flow at times of news
releases. Breedon and Vitale (2004) studied high-frequency brokered
electronic interdealer data, spanning 6 months in 2000 and 2001,
but reached conclusions different from Evans and Lyons. Long spans
of daily customer-to-dealer order flow, each from a single
institution, were used by Evans and Lyons (2005) and Froot and
Ramadorai (2005) to reach their opposite conclusions. Overall, the
existing literature has analyzed either relatively short spans of
high-frequency interdealer data from various trading platforms or
longer samples of daily single-institution customer-to-dealer
data.

The structure of the foreign exchange market has, however,
changed considerably over the past few years, yielding new data
that may help resolve some of this uncertainty. Since the late
1990s, two electronic platforms have taken a leading role in
interdealer spot trading, one offered by EBS and the other offered
by Reuters, both electronic limit order books. Importantly, on a
global scale, interdealer trading in each major currency pair has
become very highly concentrated on one of the two systems. Of the
most-traded currency pairs, the top two, euro-dollar and dollar-yen
are, across the globe, have, for several years, been traded
primarily on EBS, while the third, sterling-dollar is traded
primarily on Reuters. As a result, for instance, the current EBS
euro-dollar exchange rate is now universally used to generate
customer quotes and to price derivatives.

In this paper, we analyze a new dataset of minute-by-minute
order flow and exchange rate returns on the EBS platform for the
euro-dollar and dollar-yen currency pairs from January 1999 through
December 2004, data which have not been previously available. This
dataset represents a clear majority of global trading in the
interdealer spot market for the two major currency pairs over a
span of six years, and is unique in having all of these
features.1

Using these data, we find a strong contemporaneous association
between exchange rate returns and interdealer order flow at
intradaily and daily frequencies: a regression of daily exchange
rate returns on contemporaneous daily order flow yields a
significant positive coefficient with an R of about 45% for euro-dollar and
50% for dollar-yen, in line with previous results. However, our
long span of data gives us more power to test the strength of the
association at lower frequencies, and we find that the association
weakens notably at the one- and three-month frequencies. Thus,
longer swings in the euro-dollar and dollar-yen exchange rates over
the period do not seem to be as strongly associated with
interdealer order flow as the analysis of shorter spans of data may
have led us to believe. Next, we uncover a pattern of non-linearity
in the return-order flow relationship at very high frequency and
document an intradaily pattern to the association between
one-minute order flow and one-minute exchange rate returns 
the relationship is weakest at times when markets are most active.
Using rolling regressions, we also consider lower-frequency
time-variation in the relationship between order flow and exchange
rate returns, finding in particular that the association seems to
have weakened somewhat since 2001, likely consistent with an
improvement in market liquidity. Finally, we study patterns in
order flow around important scheduled U.S. macroeconomic data
releases. Overall, our analysis of this important dataset offers
far more support for the ``weak flow-centric'' view of the role of
order flow than for the ``strong flow-centric'' view: global
interdealer order flow has an undeniable short- and medium-term
impact on exchange rate variations, but much less explanatory power
for long-term exchange rate movements.

The plan for the remainder of this paper is as follows. In
section 2, we describe the data that we use in this paper. Section
3 reports our results on the relationship between order flow and
exchange rate movements at different frequencies. Section 4
considers non-linearity and time-variation in the link between
order flow and exchange rates, including variation over the full
sample and intradaily patterns in the association. Section 5
studies the patterns in order flow around important scheduled U.S.
macroeconomic data releases. Section 6 concludes.

2 The Data

The EBS data that we use in this study consist of three time
series at the one-minute frequency: the volume (in base currency)
of buyer-initiated trades in each minute, the volume of
seller-initiated trades in each minute, and the one-minute exchange
rate returns. The data span January 1999 to December 2004 for the
dollar-yen and euro-dollar currency pairs. These EBS data, which
are proprietary and confidential, do not contain any information on
the identity of market participants.2

A buyer-initiated trade is a transaction where a quote offering
to sell euros for dollars, or dollars for yen, placed in the EBS
system by one dealer is dealt on by another dealer, who is then
seen as the aggressor, or the initiator of the transaction (and
buys euros or yen, respectively). A seller-initiated trade is of
course defined similarly. The trades in our dataset are signed as
buyer-initiated or seller-initiated by the EBS computer system, so
we do not have to rely on an algorithm to estimate the direction of
trade. Order flow is defined as the difference between the volume
of buyer-initiated trades and that of seller-initiated trades,
measured in base currency. A positive value of order flow therefore
indicates a net excess of buyer-initiated trades. 3

We exclude all data collected from Friday 17:00 New York time
through Sunday 17:00 New York time from our sample, as foreign
exchange trading activity during these hours is minimal. We also
drop certain holidays and days of unusually light volume: December
24-December 26, December 31-January 2, Good Friday, Easter Monday,
Memorial Day, Labor Day, Thanksgiving and the following day, and
July 4 (or, if this is on a weekend, the day on which the
Independence Day holiday is observed). Similar conventions were
adopted by Andersen, Bollerslev, Diebold and Vega (2003). To
construct a minute-by-minute price series, we use the midpoint of
the best bid and the best ask price in the system at the start of
each minute and calculate continuously compounded one-minute
returns. That is, we compute our returns as 10,000 times the
one-minute change in the log exchange rate. Our returns have
therefore the interpretation of (approximately) the percentage
change in the exchange rate multiplied by 100, and so the units can
be thought of as basis points of exchange rate movements.

Table 1 reports some summary statistics. The table shows the
mean, standard deviation, minimum, and maximum of the order flow at
several frequencies ranging from one minute to one month. The
proportion of observations for which the order flow is positive at
each frequency is also shown. For both currency pairs, the mean
order flow is positive. Although it is only very slightly positive
at the one-minute frequency, it turns out that if one aggregates
to, for instance, the daily frequency, about 70 percent of days
have positive order flow. This is somewhat surprising and puzzling,
but the same phenomenon has been found by other authors including
Dunne, Hau and Moore (2004) and Evans and Lyons (2002) and, in
analysis of the GovPX Treasury market trading platform, by Brandt
and Kavajecz (2004).4 Table 2
reports autocorrelation coefficients for order flow at frequencies
ranging from one minute to one month. Order flow tends to be
positively autocorrelated at the highest frequencies. We also note
some first-order autocorrelation at the daily and weekly
frequencies in both currencies.

3 Order Flow and Exchange
Rate Returns: the Basic Relationship

To study the contemporaneous relationship between order flow and
exchange rate movements at various frequencies, we first ran
regressions of the form

(1)

where refers to the
returns over a horizon
and refers to the
order flow over that same horizon. The horizon can be one minute, or we can
aggregate both the returns and the order flow to lower frequencies.
Note that equation (1) includes an intercept,
meaning that our regressions effectively demean the order flow
series.

Figure 1 plots the estimated slope coefficients and
Rs from this
regression against , for
the euro-dollar and dollar-yen currency pairs. The plots of the
estimated slope coefficients include 95 percent confidence
intervals, constructed using heteroskedasticity-robust standard
errors.5 At the
one-minute frequency, the coefficient is significantly positive and the
R is 36% for the
euro-dollar and 30% for the dollar-yen. An excess of
buyer-initiated trades is associated with a rising price, with an
order imbalance of 1 billion (of the base currency) estimated to
lead to about a half-percent appreciation (precisely 55 basis
points in euro-dollar and 72 basis points in dollar-yen). At the
daily frequency, the Rs are about 50%, and the estimates of are very significantly positive,
about 40 basis points per billion for each currency pair, only a
bit less than the Evans and Lyons (2002) estimates. However, at the
monthly frequency, the Rs fall to about 20% and 30% for the euro-dollar
and dollar-yen currency pairs respectively, and theis estimated to be about 20
basis points. At the two- and three-month frequencies, the
Rs continue to
decline. The pattern is clear--the association between order flow
and exchange rate returns is strong at the intradaily, daily, and
weekly frequencies, but then declines gradually at lower
frequencies.6

Figure 2 shows a daily time series of the euro-dollar and
dollar-yen exchange rates and of the corresponding demeaned
cumulative order flows from 1999 through 2004. The relationship
between the two time series appears consistent with the regression
results displayed in Figure 1. An association between
high-frequency movements in order flow and exchange rates is often
apparent. Indeed, there are many periods where cumulative order
flow and exchange rates track each other quite closely. However,
overall, there is little evidence of a strong low-frequency
association between cumulative order flow and exchange rate
returns. Evans and Lyons (2002), for instance, showed graphs of
cumulative order flow and exchange rates over a four month period
and found a remarkably close relationship. Studying Figure 2, there
are clearly periods with a similarly tight link between cumulative
order flow and exchange rates, some lasting perhaps a year or more,
but there are also obvious periods of similar length with little or
no association.

Figure 3 shows cross-correlograms of returns and order flow for
both currency pairs at weekly and monthly frequencies, with leads
and lags of 30 weeks and 6 months, respectively. Critical values
for these cross-correlations to be significantly different from
zero at the 5% level are also shown. The strong positive
contemporaneous association between returns and order flow found in
the regression results is obvious. However, we also note the
presence of negative cross-correlations between returns and order
flow at a number of leads and lags in both currency pairs, often
statistically significant, particularly at a monthly frequency. The
results are also consistent with those displayed in Figures 1 and
2, with the negative autocorrelations at some weekly and monthly
intervals linked to the reduced Rs observed at longer horizons in the estimation of
equation (1).

The findings shown in Figures 1, 2, and 3, are consistent with
an interpretation of the association between exchange rate returns
and order flow as reflecting principally a temporary--although
relatively long-lasting--liquidity effect. They are also perhaps
consistent with a behavioral interpretation of the sort advocated
by authors such as Barberis and Shleifer (2003) and discussed by
Froot and Ramadorai (2005), who found that, in their sample,
institutional-investor order flow was highly correlated with
exchange rate returns at short horizons but essentially
uncorrelated at long horizons.7 But our
results appear to offer little support to the idea that order flow
has a central role in driving long-run fundamental currency values
 the ``strong flow-centric'' view. Still, it seems worth
remembering that the Rs near 0.2 that we find for our return-order flow
regressions at longer horizons would have been viewed as a great
advance in explaining exchange rate movements prior to the work of
Evans and Lyons.

Our finding that the association between returns and interdealer
order flow is simultaneously quite strong at high frequencies and
much weaker at horizons of one month and longer implies some
predictability in either exchange rate returns or order flow (or
both) at lower frequencies. But we would expect any out-of-sample
predictability of either of these variables, especially the
exchange rate, to be slight. Recently, Bacchetta and van Wincoop
(2006), proposed a model that gives a micro-founded theoretical
basis for the role of order flow in exchange rate movements. The
benchmark parameterization of this model gives monotonically
increasing Rs in the
regression of returns on order flow (our equation (1)) as the frequency of observation decreases. However,
Bacchetta and van Wincoop note that, under some other parameter
values, the relationship between returns and order flow gets weaker
at longer horizons, with Rs decreasing, consistent with our results.8

We conclude this section with a little more discussion about
what these results mean for the existence of information
asymmetries about long-run fundamentals in the foreign exchange
market. Order flow is simply a way of partitioning market
participants into two groups: the active parties (the initiators of
the trades) and the passive parties. A correlation between this
measure and long-run exchange rate movements would indicate that
the active parties had superior information about factors driving
the fundamental values of exchange rates. We find little such
correlation, implying only limited evidence for information
asymmetry between the active and passive parties in the interdealer
foreign exchange market. Our results are however quite agnostic as
to the existence of an informational advantage by some foreign
exchange market participants about the long-run fundamental values
of exchange rates. It could be that there exists some other way of
partitioning agents into informed and uninformed groups such that
the net buying orders of the informed group would be highly
correlated with long-run exchange rate movements, implying an
important information asymmetry. Such information asymmetries might
be apparent when using order flow from the customer-to-dealer
sector of the market, if certain groups of customers have
informational advantages or disadvantages, as suggested by Evans
and Lyons (2005). Still, given that the EBS data we use represent
the central site of price discovery for these two exchange rates,
our findings clearly cast some doubt on the idea that the
short-term explanatory power of order flow comes from it containing
substantial information about factors driving the long-run
fundamental values of exchange rates.

4 Non-Linearity and Variation over Time of
the Return/Order Flow Relationship

Our long span of data at very high frequency also allows us to
study the linearity and the stability of the relationship between
exchange rate returns and order flow. Figure 4 shows scatterplots
of the returns and order flow in the euro-dollar and dollar-yen
currency pairs at the one-minute and one-day frequencies, along
with fitted lines from the OLS regressions and non-parametric
estimates of the relationship derived using the Nadaraya-Watson
estimator. The scatterplots clearly show the systematic positive
relation obtained in the linear regressions, and it is obvious that
the relations at the one-minute and one-day frequencies are not the
result of a small number of outliers. However, at the one-minute
frequency, the scatterplots do suggest some nonlinearity in the
association, and this is confirmed by the non-parametric
estimation. At this very high frequency, the nature of the
nonlinearity is that large order flow imbalances, both positive and
negative, have a smaller incremental effect on the exchange rate.
This concurs with the findings of Jones, Kaul and Lipson (1994) in
equity markets. It is, however, not consistent with the idea (and
the common wisdom among traders) that large order imbalances are
more likely to convey information and should therefore have a
proportionally larger impact on prices. At the daily frequency, in
contrast, the association between returns and order flow is quite
linear, and the OLS estimates and the non-parametric estimates are
very close to each other.

The association between order flow and exchange rate returns may
vary over time, and any such time-variation may shed light on the
source and interpretation of the relationship between order flow
and exchange rates. We first studied intradaily variation in the
association between order flow and exchange rate returns. To this
end, we considered the regression

(2)

where and denote one-minute returns and
order flow, respectively, and is a dummy variable that takes on the value
1 iff observation is in
the th half-hour interval
of the day (measured in New York time). This regression was run at
the one-minute frequency. Estimates of the coefficients, along with 95 percent
confidence intervals, are shown for both currency pairs in Figure
5. Considerable variation within the day in the association between
order flow and exchange rate returns is evident. Figure 5 also
shows the average per-minute trading volume in each half-hour
window of the day.9 The
slope coefficient is lowest at times within the day when
trading is most active. For example, for the euro-dollar currency
pair, the slope coefficient is lowest between around 3am and 11am
New York time, the hours during which European and/or North
American markets are most active. This pattern of negative
correlation between trading volume and the price impact of trades
is consistent with interpreting estimates of the contemporaneous
association between high-frequency returns and high-frequency order
flow as a measure of liquidity.10

Figure 6 focuses on the lower-frequency variation over time of
the return/order flow relationship. It shows rolling regression
estimates for equation (1) with 30-day windows
at the one-minute frequency and with 250-day windows (corresponding
to about one calendar year of data) at the one-day frequency. The
resulting slope coefficient estimates, along with 95 percent
confidence intervals, are shown for both currency pairs. The
one-minute estimates over a 30-day window show a large amount of
variation over time, with the slope coefficients ranging from about
40 basis points per billion of order flow to about 80 basis points
in euro-dollar and from 40 to more than 100 basis points in
dollar-yen. This highlights the sensitivity of the estimates to the
sample period, and it may explain some of the differences found in
past work done on short samples from different time periods. The
one-day estimates over a yearly window show, of course, less
variation, but the same overall trends can be seen at both
frequencies and for both currency pairs--the estimates of the slope
coefficients rose in the early part of the sample, and peaked for
windows ending in 2001, before trending downwards
subsequently.11 If one
interprets these slope coefficients as a measure of market
liquidity, these results are consistent with the conclusion that
foreign exchange market liquidity may have decreased during the
last U.S. recession, but has improved overall since then.

5 Order Flow and
Macroeconomic News Announcements

A scheduled data release is the canonical public news event and
one might theorize that the role of asymmetric information would be
minimal at these times and that rational agents would
instantaneously impound the news in asset prices without requiring
any trading activity. To be sure, the mapping from a
multi-dimensional data release into future economic outcomes is
complex, and working out the implications of a news release is
something that could well be thought of as private information, as
in the skilled information process models of Kim and Verrecchia
(1994, 1997). However, in a rational expectations and efficient
markets framework, there should be no systematic relationship
between order flow and the headline surprise (the unexpected
component of the headline news announcement). Nonetheless, some
authors including Love and Payne (2004) and Evans and Lyons (2006)
have shown that, in their data, macroeconomic news surprises are
correlated with order flow, which, they have argued, could be
evidence of a role for order flow in price discovery at these
times. A quite mundane explanation for this correlation is,
however, also possible. Leaving live quotes near the
pre-announcement price in an anonymous limit order book at the
moment of a news announcement is surely not a profit-maximizing
strategy, as it amounts to taking a one-sided bet against oneself.
Yet, Carlson and Lo (2004), studying in great detail the impact in
the foreign exchange market of one news announcement, argue that
some foreign exchange traders choose to do precisely this.12 If the price jumps following an
announcement, any such quotes on one-side of the original price
will be swept out. Under this scenario, the observed order flow
will then be, at least in part, simply a byproduct of the price
movement arising from the direct reaction of the exchange rate to
the surprise component of the news announcement.

Using our long span of high-frequency data, we study the
behavior of order flow at times of U.S. macroeconomic announcements
that come out at 8:30am. We regress order flow from 8:30 to 8:31
and from 8:31 to 9:00 on the day of a news announcement on the
surprise component of the data that were released at 8:30 that day.
A separate regression is run for each type of data announcement.
The releases that we consider are GDP (the quarterly advance
release), and, all at monthly frequency, the employment report,
CPI, housing starts, retail sales, PPI, durable goods sales, the
trade balance and the unemployment rate. For each type of news
release, the surprise component of that release, is measured as the
difference between the actual released value and the ex-ante median
expectation taken from the Money Market Services survey, scaled by
its standard deviation. Love and Payne (2004) considered very
similar regressions, but, because of the short span of their
sample, they had to pool all the different announcement types. Our
results are shown in Table 3. The sign of the unemployment rate has
been flipped, so that, for each announcement type, a positive
surprise means stronger-than-expected economic activity. In the
8:30 to 8:31 minute, all announcement surprises are estimated to
have a negative effect on euro-dollar order flow and a positive
effect on dollar-yen order flow, meaning news of stronger economic
activity is associated with orders by market ``takers'' to buy
dollars. The association is statistically significant at the 5
percent level in a majority of cases (one of the exceptions being
the non-farm payrolls in euro-dollar). However, the headline data
surprise only impacts order flow for a very short interval after
each type of announcement. Most announcement surprises do not have
a significant correlation with the order flow from 8:31 to 9:00,
and the estimated signs of the effects are mixed.

Our results in the first minute are entirely consistent with the
findings of Love and Payne (2004) and therefore highlight the same
puzzle: in a rational expectations framework with efficient
markets, the surprise component of these data releases, released
simultaneously to all the trading public, should not be correlated
with order flow. The fact that, for both currency pairs, every
single type of macroeconomic announcement is accompanied by this
pattern in order flow is consistent with the behavior highlighted
by Carlson and Lo (2004) accounting for at least some of the order
flow seen in the first minute. The fact that there is, in contrast,
no consistent relationship in the next 29 minutes, may be further
evidence to support that interpretation.

6 Conclusion

We have studied the relationship between exchange rates and
order flow in an important new dataset from EBS which represents a
majority of global interdealer foreign exchange trading in the top
two currency pairs from 1999 to 2004. Using these data, we confirm
the presence of a strong association between exchange rate returns
and interdealer order flow at horizons of up to two weeks. The
magnitude of this association is generally in line with several
previous studies conducted on shorter spans of data. However, at
horizons beyond two weeks, the strength of the association between
exchange rate returns and order flow becomes substantially smaller
than at shorter horizons. We document a clear intraday pattern to
the sensitivity of exchange rates to order flow, with higher
sensitivity associated with periods of lower trading volume, as
well as some non-linearity in the high-frequency relationship, with
large amounts of order flow associated with proportionally smaller
exchange rate movements. Overall, our study offers more support for
the ``weak flow-centric'' view of the role of order flow in
exchange rate determination than for the ``strong flow-centric''
view: interdealer foreign exchange order flow has a strong impact
on short- and medium-term exchange rate returns, but much less
explanatory power for long-term exchange rate fluctuations.

This table reports some summary statistics for order flow (in
millions of base currency) at the one-minute frequency, and
aggregated to five-minute, hourly, daily, weekly and monthly
frequencies. Proportion positive means the number of observations
at that frequency for which the measured order flow is positive,
divided by the total number of observations for which it is
nonzero.

Table 2: Order Flow Autocorrelation Coefficients

Horizon, h: Lead

Euro

Yen

1 minute: 1

0.1956*

0.2253*

1 minute: 2

0.0779*

0.1137*

1 minute: 3

0.0376*

0.0775*

1 minute:10

0.0047*

-0.0012

1 minute: 30

-0.0039*

-0.0009

5 minute: 1

0.0873*

0.1839*

5 minute: 2

0.0125*

0.0742*

5 minute: 3

0.0064*

0.0550*

5 minute: 10

0.0047*

0.0244*

5 minute: 30

-0.0011

0.0093*

1 hour: 1

0.0096

0.1720*

1 hour: 2

-0.0025

0.0756*

1 hour: 3

-0.0058

0.0507*

1 hour: 10

0.0032

0.0235*

1 hour: 30

-0.0081

0.0118*

1 day: 1

0.1067*

0.1902*

1 day: 2

0.0245

0.1143*

1 day: 3

0.0295

0.0329

1 day: 10

0.0265

0.0280

1 day: 30

0.0300

-0.0223

1 week: 1

0.1358*

0.1861*

1 week: 2

0.0803

0.0829

1 week: 3

0.0245

0.0189

1 week: 10

-0.0400

0.1029

1 week: 30

0.0805

-0.0673

1 month: 1

0.2365*

0.0089

1 month: 2

0.0803

0.0891

1 month: 3

-0.1474

0.2431*

This table reports autocorrelation coefficients for order flow
at one-minute, five-minute, hourly, daily, weekly and monthly
frequencies. We report results for leads of 1, 2, 3, 10, and 30
intervals. * indicates statistical significance of an individual
coefficient at the 5% level.

The sign of this countercylical indicator
has been flipped. * denotes statistical significance at the 10% level, ** denotes statistical significance at the 5% level, and *** denotes statistical significance at the 1% level.

Figure 1: Estimates of Excess Returns Regressed on Order Flow at Different Horizons

The dotted line is the order flow series; the solid line is the exchange rate level

Figure 3: Cross-Correlograms of Returns and Order Flow

In each panel, the correlation at lag zero denotes the contemporaneous correlation between returns and order flow. Positive lags denote correlations between order flow and future returns. Negative lags denote correlations between order flow and past returns
Dashed lines show 5% critical values for null of zero.

Figure 4: Scatter Plot of Exchange Rate Returns and Order Flow

Dashed line is the OLS estimate; solid line is the Nadaraya-Watson nonparametric estimate

Figure 5: Intraday Regression Betas and Average Trading Volume

95 percent confidence intervals constructed using heteroscedasticity robust standard errors are also shown
Index 100: Average Volume per one-minute period over the whole sample period (seperate index for each currency pair)

Footnotes

* International Finance Division (Berger,
Chaboud) and Division of Monetary Affairs (Wright), Board of
Governors of the Federal Reserve System, Washington DC 20551,
Harvard Business School, Boston, MA, 02163 (Chernenko), and EBS,
535 Madison Avenue, New York, NY 10022 (Howorka). Contact:
jonathan.h.wright@frb.gov. We are grateful
to Jon Faust, Mico Loretan, Richard Lyons, Dagfinn Rime, Krista
Schwarz and Eric van Wincoop for helpful discussions. The views in
this paper are solely the responsibility of the authors and should
not be interpreted as reflecting the views of the Board of
Governors of the Federal Reserve System or of any person associated
with the Federal Reserve System. Return
to text

1. Killeen, Lyons, and Moore (2001)
and Hau, Killeen, and Moore (2002) have studied EBS order flow data
at daily frequency from 1998 and 1999. Breedon and Vitale (2004)
collected and studied six months of partial EBS order flow data
from 2000 and 2001. Return to
text

2. We refer the reader to Chaboud et
al. (2004) for details of the EBS trading system. Return to text

3. In the study of equity markets,
much of the microstructure literature has focused on the
signed-order count measure. We also analyzed EBS order flow data
based on counts of trades per minute, and the results, which are
very similar, are available from the authors. In practice, as the
size of most individual trades on EBS are within a small range
(almost always below $10 million), and the average trade size
varies little over time, it is not surprising that the two order
flow measures give us very similar results. Return to text

4. The phenomenon has also been
noted in equity trading platforms, where it is less surprising, as
investors naturally tend to build long equity positions.
Constraints on short sales may also contribute to the recorded
imbalance in equity markets (Diether, Lee and Werner,
2005). Return to text

5. At frequencies lower than one
day, we use overlapping observations, and control for the resulting
serial correlation in the errors by using Newey-West standard
errors. Return to text

6. We also ran regression (1) at the same horizons using excess returns instead of
simple returns as a dependent variable, that is accounting for
interest rate differentials over horizons of one day or more. LIBOR
rates in dollar, euro, and yen were used to calculate excess
returns. The results at all horizons, available from then authors
on request, were almost identical to the estimates obtained with
simple returns. This is not surprising given the typical relative
magnitudes of interest differentials (small) and exchange rate
movements (large) over these horizons in these currency pairs, in
addition to the well-known failure of uncovered interest
parity. Return to text

7. We note that the data used in
this study and those used in Froot and Ramadorai (2005) come from
very different sectors of the foreign exchange market and overlap
in time for only about 2 years. Return to
text

9. Volume refers to the total value
in base currency of all trades conducted regardless of whether they
are buyer-initiated or seller-initiated. To preserve data
confidentiality, volume is expressed in index form. EBS does not
publicly release trading volume data for individual currency
pairs. Return to text

10. Given this intradaily pattern,
one may suspect that the non-linearity observed in the one-minute
return/order flow relationship (Figure 5) could simply result from
the aggregation of observations obtained at different times of the
trading day. We find that this is not the case, however, as the
non-linearity is still evident when using only observations
obtained between 3 am and 11 am, the busiest time of the trading
day. Return to text

11. We note that, at a daily
frequency, the lowest coefficient estimate in dollar-yen is for a
one-year window ending in March 2004, precisely at the end of the
14-month-long episode of massive foreign exchange intervention by
Japan's Ministry of Finance. In contrast, Girardin and Lyons
(2006), using daily customer order flow data from Citibank, do not
detect a significant change in the return/order flow slope
coefficients during Japanese intervention. Return to text

12. Carlson and Lo (2004) argue that
these traders follow at all times a rigid strategy of attempting to
cover in the interdealer market, with only a small fixed profit
margin, positions open while trading with their
customers. Return to text